A risk measurement and management framework that takes model risk seriously Most financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. Bayesian Risk Management details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model–driven forecasts in a way that standard statistical risk measurement does not. And unlike current machine learning–based methods, the framework presented here allows you to measure risk in a fully–Bayesian setting without losing the structure afforded by parametric risk and asset–pricing models. Recognize the assumptions embodied in classical statistics Quantify model risk along multiple dimensions without backtesting Model time series without assuming stationarity Estimate state–space time series models online with simulation methods Uncover uncertainty in workhorse risk and asset–pricing models Embed Bayesian thinking about risk within a complex organization Ignoring uncertainty in risk modeling creates an illusion of mastery and fosters erroneous decision–making. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. Bayesian Risk Management provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.

This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.

Within our research group Bayesian Risk Solutions we have coined the idea of a Bayesian Risk Management (BRM). It claims (1) a more transparent and diligent data analysis as well as (2)an open-minded incorporation of human expertise in risk management. In this dissertation we formulize a framework for BRM based on the two pillars Hardcore-Bayesianism (HCB) and Softcore-Bayesianism (SCB) providing solutions for the claims above. For data analysis we favor Bayesian statistics with its Markov Chain Monte Carlo (MCMC) simulation algorithm. It provides a full illustration of data-induced uncertainty beyond classical point-estimates. We calibrate twelve different stochastic processes to four years of CO2 price data. Besides, we calculate derived risk measures (ex ante/ post value-at-risks, capital charges, option prices) and compare them to their classical counterparts. When statistics fails because of a lack of reliable data we propose our integrated Bayesian Risk Analysis (iBRA) concept. It is a basic guideline for an expertise-driven quantification of critical risks. We additionally review elicitation techniques and tools supporting experts to express their uncertainty. Unfortunately, Bayesian thinking is often blamed for its arbitrariness. Therefore, we introduce the idea of a Bayesian due diligence judging expert assessments according to their information content and their inter-subjectivity.

Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions. Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more Introduces all necessary mathematics, probability, and statistics as needed Establishes the basics of probability, risk, and building and using Bayesian network models, before going into the detailed applications A dedicated website contains exercises and worked solutions for all chapters along with numerous other resources. The AgenaRisk software contains a model library with executable versions of all of the models in the book. Lecture slides are freely available to accredited academic teachers adopting the book on their course.

Although many Bayesian Network (BN) applications are now in everyday use, BNs have not yet achieved mainstream penetration. Focusing on practical real-world problem solving and model building, as opposed to algorithms and theory, Risk Assessment and Decision Analysis with Bayesian Networks explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide powerful insights and better decision making. Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, and more Introduces all necessary mathematics, probability, and statistics as needed The book first establishes the basics of probability, risk, and building and using BN models, then goes into the detailed applications. The underlying BN algorithms appear in appendices rather than the main text since there is no need to understand them to build and use BN models. Keeping the body of the text free of intimidating mathematics, the book provides pragmatic advice about model building to ensure models are built efficiently. A dedicated website, www.BayesianRisk.com, contains executable versions of all of the models described, exercises and worked solutions for all chapters, PowerPoint slides, numerous other resources, and a free downloadable copy of the AgenaRisk software.

This book presents a risk management framework designed to achieve better decisions and more desirable outcomes. It presents an in-depth discussion of some fundamental principles of risk management related to the use of expected values, uncertainty handling, and risk acceptance criteria. Several examples from the offshore petroleum industry are included to illustrate the use of the framework, but it can also be applied in other areas.

An important guide to assessing and managing the environment from a landscape perspective Ecological relationships are nested within the landscape. Identifying the relevant spatial and temporal scales is critical for an effective understanding of ecological functions that human societies depend upon. Moreover, human encroachment into natural areas, or changes in climate, can alter spatial relationships, which in turn can negatively affect vital plant and wildlife patterns—and weaken economic structures needed to sustain human societies. This book is the first to combine multiple disciplines into one cohesive strategy to study these crucial connections, and looks toward building a social paradigm that embraces the dynamics of ecological systems. This book: Integrates landscape ecology, environmental risk assessment, valuation of ecological goods and services, and environmental management decision processes into one single source Includes chapters on quantitative measures, Bayesian modeling,¿economic analysis, and sustainable landscapes Covers marine, forest, agricultural, and pharmaceutical risk assessment Has a chapter on predicting climate change risk to ecosystems Has a companion ftp site with color graphics, animations, and risk assessment tools With material that is accessible across all knowledge levels, Environmental Risk Assessment and Management from a Landscape Perspective moves beyond looking solely at chemical contaminants to diagnose environmental threats, and aims to accomplish practical risk assessment in a manner that supports long-term sustainable management.

A comprehensive, one-stop reference for cutting-edge research in integrated risk management, modern applications, and best practices In the field of business, the ever-growing dependency on global supply chains has created new challenges that traditional risk management must be equipped to handle. Handbook of Integrated Risk Management in Global Supply Chains uses a multi-disciplinary approach to present an effective way to manage complex, diverse, and interconnected global supply chain risks. Contributions from leading academics and researchers provide an action-based framework that captures real issues, implementation challenges, and concepts emerging from industry studies.The handbook is divided into five parts: Foundations and Overview introduces risk management and discusses the impact of supply chain disruptions on corporate performance Integrated Risk Management: Operations and Finance Interface explores the joint use of operational and financial hedging of commodity price uncertainties Supply Chain Finance discusses financing alternatives and the role of financial services in procurement contracts; inventory management and capital structure; and bank financing of inventories Operational Risk Management Strategies outlines supply risks and challenges in decentralized supply chains, such as competition and misalignment of incentives between buyers and suppliers Industrial Applications presents examples and case studies that showcase the discussed methodologies Each topic's presentation includes an introduction, key theories, formulas, and applications. Discussions conclude with a summary of the main concepts, a real-world example, and professional insights into common challenges and best practices. Handbook of Integrated Risk Management in Global Supply Chains is an essential reference for academics and practitioners in the areas of supply chain management, global logistics, management science, and industrial engineering who gather, analyze, and draw results from data. The handbook is also a suitable supplement for operations research, risk management, and financial engineering courses at the upper-undergraduate and graduate levels.

This is the Proceedings of the Eighth International Conference on Management Science and Engineering Management (ICMSEM) held from July 25 to 27, 2014 at Universidade Nova de Lisboa, Lisbon, Portugal and organized by International Society of Management Science and Engineering Management (ISMSEM), Sichuan University (Chengdu, China) and Universidade Nova de Lisboa (Lisbon, Portugal). The goals of the conference are to foster international research collaborations in Management Science and Engineering Management as well as to provide a forum to present current findings. A total number of 138 papers from 14 countries are selected for the proceedings by the conference scientific committee through rigorous referee review. The selected papers in the second volume are focused on Computing and Engineering Management covering areas of Computing Methodology, Project Management, Industrial Engineering and Information Technology.

Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk management—since these are the areas in finance where Bayesian methods have had the greatest penetration to date.

The management of operational risk in the banking industry has undergone explosive changes over the last decade due to substantial changes in the operational environment. Globalization, deregulation, the use of complex financial products, and changes in information technology have resulted in exposure to new risks which are very different from market and credit risks. In response, the Basel Committee on Banking Supervision has developed a new regulatory framework for capital measurement and standards for the banking sector. This has formally defined operational risk and introduced corresponding capital requirements. Many banks are undertaking quantitative modelling of operational risk using the Loss Distribution Approach (LDA) based on statistical quantification of the frequency and severity of operational risk losses. There are a number of unresolved methodological challenges in the LDA implementation. Overall, the area of quantitative operational risk is very new and different methods are under hot debate. This book is devoted to quantitative issues in LDA. In particular, the use of Bayesian inference is the main focus. Though it is very new in this area, the Bayesian approach is well suited for modelling operational risk, as it allows for a consistent and convenient statistical framework for quantifying the uncertainties involved. It also allows for the combination of expert opinion with historical internal and external data in estimation procedures. These are critical, especially for low-frequency/high-impact operational risks. This book is aimed at practitioners in risk management, academic researchers in financial mathematics, banking industry regulators and advanced graduate students in the area. It is a must-read for anyone who works, teaches or does research in the area of financial risk.

Floods cause distress and damage wherever and whenever they happen. Flooding from rivers, estuaries and the sea threatens many millions of people worldwide and economic and insurance losses from flooding have increased significantly since 1990. Across the European Union, flood management policy is changing in response to the EU Directive on the assessment and management of flood risks, which requires a move from flood protection and defence to comprehensive flood risk management. Flood Risk Management: Research and Practice includes about 200 contributions from the international conference FLOODrisk 2008 (Oxford, UK, 30 September – 2 October 2008). FLOODrisk 2008 was an initiative of the FLOODsite research project on Integrated Flood Risk Analysis and Management Methodologies. FLOODsite was a major “Integrated Project” in the European Commission Sixth Framework Programme; contract number GOCE-CT-2004-505420. The conference provided a forum for leading researchers, flood risk managers, policy makers and practitioners from government, commercial and research organisations to gain an overview of advances in this important subject. Flood risk management practice crosses several professions and disciplines and these are represented in the breadth of the scope of the conference and these proceedings. The conference covered all aspects of flood risk: the causes of floods, their impacts on people, property and the environment, and portfolios of risk management measuresm, while the principal themes included: climate change, estimation of extremes, flash floods, flood forecasting and warning, inundation modelling, systems analysis, uncertainty, international programmes, flood defence infrastructure and assets, environmental impacts, human and social impacts, vulnerability and resilience, risk sharing, equity and social justice, and, civil contingency planning and emergency management. Flood Risk Management: Research and Practice will be of interest to an international readership, ranging from authorities, consultants and engineers involved in flood management; researchers, post graduate lecturers and students, to policy makers, particularly at national level.

Risk Assessment and Decision Making in Business and Industry: A Practical Guide presents an accessible treatment of the procedures and technologies involved in designing and building risk-assessment processes and models. Areas examined include: brokerage-house portfolio management legal decision making construction oil/gas exploration environmental assessments engineering marketing government manufacturing The entire volume is presented as a narrative, keeping statistical jargon to a minimum and explaining all concepts, techniques, and processes in a straightforward manner. The author emphasizes that the technical aspects of a risk-assessment and decision-making effort are secondary to the cultural, organizational, and interpersonal facets of establishing a framework. "Practical" is the operative term throughout the text. Risk Assessment and Decision Making in Business and Industry: A Practical Guide enables readers who are not risk experts to effect an easy execution of the risk model building effort.

This book is about the legal, economical, and practical assessment and management of risky activities arising from routine, catastrophic environmental and occupational exposures to hazardous agents. It includes a discussion of aspects of US and European Union law concerning risky activities, and then develops the economic analyses that are relevant to implementing choices within a supply and demand framework. The book also discusses exposure-response and time-series models used in assessing air and water pollution, as well as probabilistic cancer models, including toxicological compartmental, pharmaco-kinetic models and epidemiological relative risks and odds ratios-based models. Statistical methods to measure agreement, correlation and discordance are also developed. The methods and criteria of decision-analysis, including several measures of value of information (VOI) conclude the expositions. This book is an excellent text for students studying risk assessment and management.

Communication of risks within a transparent and accountable framework is essential in view of increasing mobility and the complexity of the modern society and the field of geotechnical engineering does not form an exception. As a result, modern risk assessment and management are required in all aspects of geotechnical issues, such as planning, design, construction of geotechnical structures, mitigation of geo-hazards, management of large construction projects, maintenance of structures and life-cycle cost evaluation. This volume discusses: 1. Evaluation and control of uncertainties through investigation, design and construction of geotechnical structures; 2. Performance-based specifications, reliability based design and limit state design of geotechnical structures, and design code developments; 3. Risk assessment and management of geo-hazards, such as landslides, earthquakes, debris flow, etc.; 4. Risk management issues concerning large geotechnical construction projects; 5. Repair and maintenance strategies of geotechnical structures. Intended for researchers and practitioners in geotechnical, geological, infrastructure and construction engineering.
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